A novel SSD-based architecture called the Pooling Pyramid Network (PPN) whose model size is >3x smaller than that of SSD MobileNet v1 with minimal loss in accuracy.

Additionally, we are releasing pre-trained weights for each of the above models based on the COCO dataset.

Accelerated Training via Cloud TPUsUsers spend a great deal of time on optimizing hyperparameters and retraining object detection models, therefore having fast turnaround times on experiments is critical. The models released today belong to the single shot detector (SSD) class of architectures that are optimized for training on Cloud TPUs. For example, we can now train a ResNet-50 based RetinaNet model to achieve 35% mean Average Precision (mAP) on the COCO dataset in < 3.5 hrs.

Accelerated Inference via Quantization and TensorFlow Lite To better support low-latency requirements on mobile and embedded devices, the models we are providing are now natively compatible with TensorFlow Lite, which enables on-device machine learning inference with low latency and a small binary size. As part of this, we have implemented: (1) model quantization and (2) detection-specific operations natively in TensorFlow Lite. Our model quantization follows the strategy outlined in Jacob et al. (2018) and the whitepaper by Krishnamoorthi (2018) which applies quantization to both model weights and activations at training and inference time, yielding smaller models that run faster.

Quantized detection models are faster and smaller (e.g., a quantized 75% depth-reduced SSD Mobilenet model runs at >15 fps on a Pixel 2 CPU with a 4.2 Mb footprint) with minimal loss in detection accuracy compared to the full floating point model.

Try it Yourself with a New Tutorial!To get started training your own model on Cloud TPUs, check out our new tutorial! This walkthrough will take you through the process of training a quantized pet face detector on Cloud TPU then exporting it to an Android phone for inference via TensorFlow Lite conversion.

We hope that these new additions will help make high-quality computer vision models accessible to anyone wishing to solve an object detection problem, and provide a more seamless user experience, from training a model with quantization to exporting to a TensorFlow Lite model ready for on-device deployment. We would like to thank everyone in the community who have contributed features and bug fixes. As always, contributions to the codebase are welcome, and please stay tuned for more updates!